Introductory words

Large parts of this tutorial follow a blog entry called Beautiful plotting in R: A ggplot2 cheatsheet by zev@zevross.com, posted on 4. August 2014. You can find the blog entry here.

Most changes were made to follow the R style guide, to change style and aesthetics of plots to be (more) beautiful and meaningful as well as to include additional tipps. Beside that, data import and setup was modified to RDS.

Preparation

You can download the data we are using in this post here.
You can find an R script with the code executed in this script here.
You need to install the following packages to execute this tutorial by using install.packages("package-name"):
- ggplot2
- ggthemes
- extrafont
- grid
- gridExtra
- reshape2

Loading `ggplot2`

library(ggplot2)

The Dataset

We are using data from the National Morbidity and Mortality Air Pollution Study (NMMAPS). To make the plots manageable we are limiting the data to Chicago and 1997-2000. For more detail on this dataset, consult Roger Pengâs book Statistical Methods in Environmental Epidemiology with R.

chic <- readRDS("chicago-nmmaps.Rds")
str(chic)

## 'data.frame':    1461 obs. of  10 variables:
##  $ city    : chr  "chic" "chic" "chic" "chic" ...
##  $ date    : Date, format: "1997-01-01" "1997-01-02" "1997-01-03" "1997-01-04" ...
##  $ death   : int  137 123 127 146 102 127 116 118 148 121 ...
##  $ temp    : num  36 45 40 51.5 27 17 16 19 26 16 ...
##  $ dewpoint: num  37.5 47.2 38 45.5 11.2 ...
##  $ pm10    : num  13.1 41.9 27 25.1 15.3 ...
##  $ o3      : num  5.66 5.53 6.29 7.54 20.76 ...
##  $ time    : int  3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 ...
##  $ season  : chr  "Winter" "Winter" "Winter" "Winter" ...
##  $ year    : chr  "1997" "1997" "1997" "1997" ...

head(chic, 10)

##      city       date death temp dewpoint      pm10        o3 time season year
## 3654 chic 1997-01-01   137 36.0   37.500 13.052268  5.659256 3654 Winter 1997
## 3655 chic 1997-01-02   123 45.0   47.250 41.948600  5.525417 3655 Winter 1997
## 3656 chic 1997-01-03   127 40.0   38.000 27.041751  6.288548 3656 Winter 1997
## 3657 chic 1997-01-04   146 51.5   45.500 25.072573  7.537758 3657 Winter 1997
## 3658 chic 1997-01-05   102 27.0   11.250 15.343121 20.760798 3658 Winter 1997
## 3659 chic 1997-01-06   127 17.0    5.750  9.364655 14.940874 3659 Winter 1997
## 3660 chic 1997-01-07   116 16.0    7.000 20.228428 11.920985 3660 Winter 1997
## 3661 chic 1997-01-08   118 19.0   17.750 33.134819  8.678477 3661 Winter 1997
## 3662 chic 1997-01-09   148 26.0   24.000 12.118381 13.355892 3662 Winter 1997
## 3663 chic 1997-01-10   121 16.0    5.375 24.761534 10.448264 3663 Winter 1997

A Default ggplot

ggplot2 syntax is fidderent from base R. We always start to define a plotting element and calling ggplot(data, aes(variable1, variable1)) which just tells ggplot2 that we are going to work with that data. Thus, only a panel is created if we only call this since ggplot2 does not now how we want to plot that data.

g <- ggplot(chic, aes(date, temp))
g

So let’s tell ggplot the style we want to use:

g + geom_point()

(No worries, I will introduce several plot types later.)

Change Color of Points

Within this command, you already can insert aesthetics as changing the color of your points:

g <- g + geom_point(color = "firebrick")
g

By applying that to our plotting element, the following plots based on g will have red points.

Working with Axes

Add Axis Labels

g <- g + labs(x = "Date", y = expression(paste("Temperature (", degree ~ F, ")")))
g

Again, we are updating our plotting element g (which means axes labels will be the same in the plots following afterwards).

Get Rid of Axis Ticks & Tick Text

g + theme(axis.ticks.y = element_blank(), axis.text.y = element_blank())

theme() is an essential command to modify all kinds of theme elements (texts and titles, boxes, symbols, backgrounds, …). We will use a lot of them – to see what is possible have a look here.

Change Size & Angle of Tick Text

g + theme(axis.text.x = element_text(angle = 50, size = 16, vjust = 0.5))

Using vjust you can adjust the position of the text (0 = left-alligned, 0.5 = centered, 1 = right-alligned).

Move Labels Away From The Plot & Change Color

g + theme(axis.title.x = element_text(color = "sienna", size = 15, vjust = -0.35),
          axis.title.y = element_text(color = "orangered", size = 15, vjust = 0.35))

Limit Axis Range

g + ylim(c(0, 50))

Alternatively you can use g + scale_x_continuous(limits = c(0, 50)) or g + coord_cartesian(xlim = c(0, 50)). The former removes all data points outside the range and second adjusts the visible area.

Axes with Same Scaling

For demonstrating purposes, let’s plot Temperature against Temperature with some random noise.

ggplot(chic, aes(temp, temp + rnorm(nrow(chic), sd = 20))) +
   geom_point() +
   labs(x = "Temperature") +
   xlim(c(0, 150)) + ylim(c(0, 150)) +
   coord_equal()

Use a Function to Alter Labels

Sometimes it is handy to alter your labels a little, perhaps adding units or percent signs without adding them to your data. You can use a function in this case. Here is an example:

ggplot(chic, aes(date, temp)) +
   geom_point(color = "firebrick") +
   labs(x = "Year", y = "Temperature") +
   scale_y_continuous(label = function(x) {return(paste(x, "Degrees Fahrenheit"))})

Working with Titles

Add a Title

g <- g + ggtitle("Temperatures in Chicago")
g

Alternatively, you can use g + labs("Temperatures in Chicago").

Make Title Bold & Add a Space at the Baseline

g <- g + theme(plot.title = element_text(size = 15, face = "bold", margin = margin(10, 0, 10, 0)))  ## top, right, bottom, left
g

The margin argument uses the margin function and you provide the top, right, bottom and left margins (the default unit is points).

Adjust Position of Titles

Allignement is controlled by hjust (which stands for horizontal adjustment):

g + theme(plot.title = element_text(size = 15, face = 4, hjust = 0))

Use a Non-Traditional Font in Your Title

Note that you can also use different fonts. To use fonts which are installed on your machine (and you may be using in your office program) we get help from a package called extrafont. It is not as easy as it seems here, check out this post if you need to use different fonts.

After we loaded the package, you need to import and load the fonts ofinstalled on your device:

library(extrafont)
font_import()

## Importing fonts may take a few minutes, depending on the number of fonts and the speed of the system.
## Continue? [y/n]

loadfonts(device = "win")

You can have a look on your imported font libary, by typing fonts() or fonttable().

Now, we can use one of those font families:

g + theme(plot.title = element_text(size = 20, family = "Times New Roman"))

Change Spacing in Multi-Line Text

You can use the lineheight argument to change the spacing between lines. In this example, Iâve squished the lines together a bit (lineheight < 1).

g + ggtitle("Temperatures in Chicago\nfrom 1997 to 2001") + 
         theme(plot.title = element_text(size = 20, face = "bold", vjust = 1, lineheight = 0.75))

Working with Legends

We will color code the plot based on season. You can see that by default the legend title is what we specified in the color argument.

ggplot(chic, aes(date, temp, color = factor(season))) +
   geom_point() +
   labs(x = "Year", y = "Temperature")

Change Order of Legend Keys

We can archieve this by changing the levels of season:

chic$season <- factor(chic$season, levels = c("Spring", "Summer", "Autumn", "Winter"))

g <- ggplot(chic, aes(date, temp, color = factor(season))) +
         geom_point() +
         labs(x = "Year", y = "Temperature")
g

Turn Off Legend Titles

g + theme(legend.title = element_blank())

Change Style of Legend Titles

g + theme(legend.title = element_text(colour = "chocolate", size = 14, face = "bold"))

Change Legend Title

The legend details can be changed via scale_color_discrete or scale_color_continuous depending on the type of variable displaying.

g + theme(legend.title = element_text(colour = "chocolate", size = 14, face = "bold")) +
    scale_color_discrete(name = "Seasons\nindicated\nby colors:")

Note that you can use the short command which is scale_color_discrete("Seasons\nindicated\nby colors:"). In most cases the string is interpreted as name (but sometimes you need to include it e.g. when using custom themes).

Change Legend Labels

We are going to replace the seasons by the months which they are covering:

g + theme(legend.title = element_text(colour = "chocolate", size = 14, face = 2)) +
    scale_color_discrete("Seasons:", labels=c("Mar - May", "Jun - Aug", "Sep - Nov", "Dec - Feb"))

Change Background Boxes in the Legend

g + theme(legend.key = element_rect(fill = "darkgoldenrod1"),
          legend.title = element_text(colour = "chocolate", size = 14, face = 2)) +
    scale_color_discrete("Seasons:")

If you want to get rid of them entirely use fill = NA.

Change Size of Legend Symbols

Points in the legend get a little lost, especially without the boxes. To override the default try:

g + theme(legend.key = element_rect(fill = NA),
         legend.title = element_text(colour = "chocolate", size = 14, face = 2)) +
    scale_color_discrete("Seasons:") +
    guides(color = guide_legend(override.aes = list(size = 6)))

Leave a Layer Off the Legend

Let’s say you have a point layer and you add label text to it. By default, both the points and the label text end up in the legend like this:

g + geom_text(data = chic, aes(date, temp, label = round(temp)), size = 4) +
    theme(legend.title = element_text(colour = "chocolate", size = 14, face = 2)) +
    scale_color_discrete("Seasons:")

You can use show.legend = F to turn a layer off in the legend:

g + geom_text(data = chic, aes(date, temp, label = round(temp), size = 4), show.legend = F) +
    theme(legend.title = element_text(colour = "chocolate", size = 14, face = 2)) +
    scale_color_discrete("Seasons:")

Manually Adding Legend Items

ggplot2 will not add a legend automatically unless you map aethetics (color, size etc) to a variable. There are times, though, that I want to have a legend so that it is clear what you are plotting.

Here is the default:

ggplot(chic, aes(x = date, y = o3)) +
   geom_line(color = "grey") +
   geom_point(color = "red") +
   labs(x = "Year", y = "Ozone")

We can force a legend by mapping to a âvariableâ. We are mapping the lines and the points using aes and we are mapping not to a variable in our dataset but to a single string (so that we get just one color for each).

ggplot(chic, aes(x = date, y = o3)) +
   geom_line(aes(color = "line")) +
   geom_point(aes(color = "points")) +
   labs(x = "Year", y = "Ozone") +
   scale_color_discrete("Type:")

We are getting close but this is not what we want. We want grey and red! To change the color, we use scale_colour_manual(). Additionally, we override the legend aesthetics using the guide() function.

Voila! Now, we have a plot with frey lines and red pints as well as a single grey line and a single red point as legend symbols:

ggplot(chic, aes(x = date, y = o3)) + 
   geom_line(aes(color = "line")) +  
   geom_point(aes(color = "points")) +
   labs(x = "Year", y = "Ozone") +
   scale_color_manual("", values = c("points" = "red", "line" = "grey"), guide = "legend") +
   guides(colour = guide_legend(override.aes = list(linetype = c(1, 0), shape = c(NA, 16))))

Working with Backgrounds

There are ways to change the entire look of your plot with one function (see below) but if you want to simply change the colors of some elelments, you can also do that.

Change the Panel Color

ggplot(chic, aes(date, temp)) +
   geom_point(color = "firebrick") +
   labs(x = "Year", y = "Temperature") +
   theme(panel.background = element_rect(fill = "grey60"))

Change Grid Lines

There are two types of grid lines: major grid lines indicating the ticks and minor grid lines between the major ones.

ggplot(chic, aes(date, temp)) +
   geom_point(color = "firebrick") +
   labs(x = "Year", y = "Temperature") +
   theme(panel.background = element_rect(fill = "grey60"),
         panel.grid.major = element_line(colour = "orange", size = 1.5),
         panel.grid.minor = element_line(colour = "indianred"))

Change the Plot Background Color

ggplot(chic, aes(date, temp)) +
   geom_point(color = "firebrick") +
   labs(x = "Year", y = "Temperature") +
   theme(plot.background = element_rect(fill = "grey60"))

Working with Margins

Sometimes it is useful to add a little space to the plot margin. Similar to the previous examples we can use an argument to the theme() function. In this case the argument is plot.margin. As In the previous example we already illustrated the default margin by changing the background color using plot.background.

Now let us add extra space to both the left and right. The argument, plot.margin, can handle a variety of different units (cm, inches, etc.) but it requires the use of the function unit from the package grid to specify the units. Here I am using a 5 cm margin on the right and left.

ggplot(chic, aes(date, temp)) +
   geom_point(color = "chocolate") +
   labs(x = "Year", y = "Temperature") +
   theme(plot.background = element_rect(fill = "grey60"),
         plot.margin = unit(c(1, 5, 1, 5), "cm"))  ## top, right, bottom, left

Working with Multi-Panel Plots

The ggplot2 package has two nice functions for creating multi-panel plots. They are related but a little different facet_wrap creates essentially a ribbon of plots based on a single variable while facet_grid can take two variables.

Create a Single Row of Plots Based on One Variable

ggplot(chic, aes(date, temp)) +
   geom_point(color = "chartreuse4") +
   labs(x = "Year", y = "Temperature") +
   facet_wrap(~year, nrow = 1)

Create a Matrix of Plots Based on One Variable

ggplot(chic, aes(date, temp)) +
   geom_point(color = "chartreuse4") +
   labs(x = "Year", y = "Temperature") +
   facet_wrap(~year, nrow = 2)

Allow Scales to Roam Free

The default for multi-panel plots in ggplot2 is to use equivalent scales in each panel. But sometimes you want to allow a panelâs own data to determine the scale. This is not often a good idea since it may give your user the wrong impression about the data but to do this you can set scales = "free" like this:

ggplot(chic, aes(date, temp)) +
   geom_point(color = "chartreuse4") +
   labs(x = "Year", y = "Temperature") +
   facet_wrap(~year, nrow = 2, scales = "free")

Note that both, x and y axes differ in their range!

Create a Grid of Plots Based on Two Variables

ggplot(chic, aes(date, temp)) +
   geom_point(color = "orangered") +
   labs(x = "Year", y = "Temperature") +
   facet_grid(year~season)

To change from row to column arrangement you can change facet_grid(year~season) to facet_grid(season~year).

Put Two (Different) Plots Side by Side

Doing this is not nearly as straightforward as traditional (base) graphics. Here are two approaches:

p1 <- ggplot(chic, aes(date, temp, color = factor(season))) + 
         geom_point() + labs(x = "Year", y = "Temperature") + guides(colour = F) 
p2 <- ggplot(chic, aes(x = date, y = o3)) + 
         geom_line(color = "grey") + geom_point(color = "red") + 
         labs(x = "Year", y = "Ozone")

library(grid)
pushViewport(viewport(layout = grid.layout(1, 2)))
print(p1, vp = viewport(layout.pos.row = 1, layout.pos.col = 1))
print(p2, vp = viewport(layout.pos.row = 1, layout.pos.col = 2))

Alternatively, this way might be a little bit easier (but now including legends — but that’s independent from the method):

p1 <- ggplot(chic, aes(date, temp, color = factor(season))) + 
         geom_point() + labs(x = "Year", y = "Temperature") + 
         theme(legend.title = element_blank())
p2 <- ggplot(chic, aes(x = date, y = o3)) + 
         geom_line(aes(color = "line")) + geom_point(aes(color = "points")) + 
         labs(x = "Year", y = "Ozone") +
         scale_color_manual("", values = c("points" = "red", "line" = "grey"), guide = "legend") +
         guides(colour = guide_legend(override.aes = list(linetype = c(1, 0), shape = c(NA, 16))))

library(gridExtra)
grid.arrange(p1, p2, ncol = 2)

Working with Themes

Use a custom theme

You can change the entire look of the plots by using custom theme. As an example, Jeffrey Arnold has put together the library ggthemes with several custom themes. For a list you can visit the ggthemes site. Without any coding you can just adapt several styles, some of them well known for their style and aesthetics.

Here is an example copying the plotting style in the The Economist magazine:

library(ggthemes)

ggplot(chic, aes(date, temp, color = factor(season))) +
   geom_point() +
   labs(x = "Year", y = "Temperature") + 
   ggtitle("Ups and Downs of Chicagos Daily Temperatures") +
   theme_economist() + 
   scale_colour_economist(name = "Seasons:") +
   theme(legend.title = element_text(size = 12, face = "bold"))

Another example is the plotting style of Tufte a minimal ink theme based on Edward Tufteâs book The Visual Display of Quantitative Information. This is the book that popularized Minard’s chart depicting Napoleon’s march on Russia as one of the âbest statistical drawings ever createdâ. His plots became famous due to the purism in their style. But see yourself:

set.seed(2017)
chic.red <- chic[sample(nrow(chic), 50), ]

ggplot(chic.red, aes(temp, o3)) +
   geom_point() +
   labs(x = "Temperature", y = "Ozone") + 
   ggtitle("Temperature and Ozone Levels in Chicago") +
   theme_tufte() +
   stat_smooth(method = "lm", col = "black", size = 0.7, fill = "gray60", alpha = 0.2)

Since Tufte’s style is about minimalism, we first reduced the number of data points shown to (at least) try to follow his rules. (Do not care about that stat_smooth command, I will explain it later. Just added it to make plot more interesting.)

If you like the way of plotting have a look on this blog entry recreating several Tufte plots in R.

Change the Size of All Plot Text Elements

Personally, I find default size of the tick text, legends and other elements to be a little too small. Luckily itâs incredibly easy to change the size of all the text elements at once. If you look below at the section on creating a custom theme youâll notice that the sizes of all the elements are relative (rel()) to the base_size. As a result, you can simply change the base_size and youâre done. Here is the code:

theme_set(theme_gray(base_size = 30))

ggplot(chic, aes(date, temp, color = factor(season))) + 
   geom_point() + 
   labs(x = "Year", y = "Temperature") + 
   guides(colour = F)

Creating a Custom Theme

If you want to change the theme for an entire session you can use theme_set as in theme_set(theme_bw()). The default is called theme_gray. If you wanted to create your own custom theme, you could extract the code directly from the gray theme and modify. Note that the rel() function change the sizes relative to the base_size.

theme_gray

## function (base_size = 11, base_family = "") 
## {
##     half_line <- base_size/2
##     theme(line = element_line(colour = "black", size = 0.5, linetype = 1, 
##         lineend = "butt"), rect = element_rect(fill = "white", 
##         colour = "black", size = 0.5, linetype = 1), text = element_text(family = base_family, 
##         face = "plain", colour = "black", size = base_size, lineheight = 0.9, 
##         hjust = 0.5, vjust = 0.5, angle = 0, margin = margin(), 
##         debug = FALSE), axis.line = element_line(), axis.line.x = element_blank(), 
##         axis.line.y = element_blank(), axis.text = element_text(size = rel(0.8), 
##             colour = "grey30"), axis.text.x = element_text(margin = margin(t = 0.8 * 
##             half_line/2), vjust = 1), axis.text.y = element_text(margin = margin(r = 0.8 * 
##             half_line/2), hjust = 1), axis.ticks = element_line(colour = "grey20"), 
##         axis.ticks.length = unit(half_line/2, "pt"), axis.title.x = element_text(margin = margin(t = 0.8 * 
##             half_line, b = 0.8 * half_line/2)), axis.title.y = element_text(angle = 90, 
##             margin = margin(r = 0.8 * half_line, l = 0.8 * half_line/2)), 
##         legend.background = element_rect(colour = NA), legend.margin = unit(0.2, 
##             "cm"), legend.key = element_rect(fill = "grey95", 
##             colour = "white"), legend.key.size = unit(1.2, "lines"), 
##         legend.key.height = NULL, legend.key.width = NULL, legend.text = element_text(size = rel(0.8)), 
##         legend.text.align = NULL, legend.title = element_text(hjust = 0), 
##         legend.title.align = NULL, legend.position = "right", 
##         legend.direction = NULL, legend.justification = "center", 
##         legend.box = NULL, panel.background = element_rect(fill = "grey92", 
##             colour = NA), panel.border = element_blank(), panel.grid.major = element_line(colour = "white"), 
##         panel.grid.minor = element_line(colour = "white", size = 0.25), 
##         panel.margin = unit(half_line, "pt"), panel.margin.x = NULL, 
##         panel.margin.y = NULL, panel.ontop = FALSE, strip.background = element_rect(fill = "grey85", 
##             colour = NA), strip.text = element_text(colour = "grey10", 
##             size = rel(0.8)), strip.text.x = element_text(margin = margin(t = half_line, 
##             b = half_line)), strip.text.y = element_text(angle = -90, 
##             margin = margin(l = half_line, r = half_line)), strip.switch.pad.grid = unit(0.1, 
##             "cm"), strip.switch.pad.wrap = unit(0.1, "cm"), plot.background = element_rect(colour = "white"), 
##         plot.title = element_text(size = rel(1.2), margin = margin(b = half_line * 
##             1.2)), plot.margin = margin(half_line, half_line, 
##             half_line, half_line), complete = TRUE)
## }
## <environment: namespace:ggplot2>

Now, let us modify the default theme function and have a look at the result:

theme_gray.mod <- function (base_size = 12, base_family = "") 
{
   half_line <- base_size/2
   theme(line = element_line(colour = "black", size = 0.5, linetype = 1, lineend = "butt"), 
         rect = element_rect(fill = "white", colour = "black", size = 0.5, linetype = 1), 
         text = element_text(family = base_family, face = "plain", colour = "black", size = base_size, 
            lineheight = 0.9, hjust = 0.5, vjust = 0.5, angle = 0, margin = margin(), debug = FALSE), 
         axis.line = element_line(), 
         axis.line.x = element_blank(), 
         axis.line.y = element_blank(), axis.text = element_text(size = rel(0.8), colour = "grey30"), 
         
         ## modified aesthetics of axes texts, ticks and titles
         axis.text.x = element_text(margin = margin(t = 0.8 * half_line/2), vjust = 1, size = 12, face = "bold"),
         axis.text.y = element_text(margin = margin(r = 0.8 * half_line/2), hjust = 1, size = 12, face = "bold"),
         axis.ticks = element_line(colour = "darkorange", size = 1.2),
         axis.ticks.length = unit(half_line, "pt"),
         axis.title.x = element_text(margin = margin(t = 0.8 * half_line, b = 0.8 * half_line/2), size = 15),
         axis.title.y = element_text(angle = 90, margin = margin(r = 0.8 * half_line, 
            l = 0.8 * half_line/2), size = 15),
         
         legend.background = element_rect(colour = NA), 
         legend.margin = unit(0.2, "cm"), 
         legend.key = element_rect(fill = "grey95", colour = "white"), 
         legend.key.size = unit(1.2, "lines"), 
         legend.key.height = NULL, 
         legend.key.width = NULL, 
         legend.text = element_text(size = rel(0.8)), 
         legend.text.align = NULL, 
         legend.title = element_text(hjust = 0), 
         legend.title.align = NULL, 
         legend.position = "right", 
         legend.direction = NULL, 
         legend.justification = "center", 
         legend.box = NULL, 
         
         ## modified aesthetics of the panel and grid
         panel.background = element_rect(fill = "white", colour = NA),
         panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
         panel.grid.major = element_line(colour = "darkorange", size = 1.2),
         panel.grid.minor = element_line(colour = "darkorange", size = 0.1),
         
         panel.margin = unit(half_line, "pt"), 
         panel.margin.x = NULL, 
         panel.margin.y = NULL, 
         panel.ontop = FALSE, 
         strip.background = element_rect(fill = "grey85", colour = NA), 
         strip.text = element_text(colour = "grey10", size = rel(0.8)),
         strip.text.x = element_text(margin = margin(t = half_line, b = half_line)), 
         strip.text.y = element_text(angle = -90, 
         margin = margin(l = half_line, r = half_line)), 
         strip.switch.pad.grid = unit(0.1, "cm"), 
         strip.switch.pad.wrap = unit(0.1, "cm"), 
         plot.background = element_rect(colour = "white"), 
         plot.title = element_text(size = rel(1.2), margin = margin(b = half_line * 1.2)), 
         plot.margin = margin(half_line, half_line, half_line, half_line), 
         complete = TRUE)
}

Have a look on the modified aesthetics with its new look of panel and gridlines as well axes ticks, texts and titles:

theme_set(theme_gray.mod())

ggplot(chic, aes(date, temp, color = factor(season))) + 
   geom_point() + labs(x = "Year", y = "Temperature") + guides(colour = F)

You can also set quick changes using theme_update:

theme_gray.mod <- theme_update(panel.background = element_rect(fill = "gray50"))

ggplot(chic, aes(date, temp, color = factor(season))) + 
   geom_point() + labs(x = "Year", y = "Temperature") + guides(colour = F)

For further exercises, we are going to reset the theme to its default:

theme_set(theme_gray())

Working with Colors

For simple applications working with colors is straightforward in ggplot2 but when you have more advanced needs it can be a challenge. For a more advanced treatment of the topic you should probably get your hands on Hadleyâs book which has nice coverage. There are a few other good sources including the R Cookbook and the ggplot2 online docs. Tian Zheng at Columbia has created a useful PDF of R colors.

In order to use color with your data, most importantly, you need to know if youâre dealing with a categorical or continuous variable.

Categorical Variables: Manually Select Colors

g <- ggplot(chic, aes(date, temp, color = factor(season))) +
      geom_point() + 
      labs(x = "Year", y = "Temperature") +
      theme(legend.title = element_blank()) +
      scale_color_manual(values = c("dodgerblue4", "darkolivegreen4", "darkorchid3", "goldenrod1"))
g

Categorical Variables: Use Built-In Palettes

g + scale_color_brewer(palette = "Set1")

You can ignore the message in the console, replacing the xisting scale is what we want.

Categorical Variables: Use Tableau colors based on `ggthemes`

library(ggthemes)

g + scale_color_tableau()

Continuous Variables: Manually Select Colors

In our example we will change the color variable to ozone, a continuous variable that is strongly related to temperature (higher temperature = higher ozone). The function scale_color_gradient() is a sequential gradient while scale_color_gradient2() is diverging.

Here is the default ggplot2 continuous color scheme (sequential color scheme):

g <- ggplot(chic, aes(date, temp, color = o3)) + 
         geom_point() + 
         labs(x = "Year", y = "Temperature") +
         scale_color_continuous("Ozone:")
g

This code produces the same plot:

ggplot(chic, aes(date, temp, color = o3)) +  
   geom_point() + 
   labs(x = "Year", y = "Ozone") +
   scale_color_gradient()

Continuous Variables: Manually Set a Sequential Color Scheme

g + scale_color_gradient(low = "darkkhaki", high = "darkgreen", "Ozone:")

Temperature data is normally distributed so how about a diverging color scheme (rather than sequential). For diverging color you can use the scale_color_gradient2 function.

mid <- max(chic$o3) / 2  ## or mid <- mean(chic$o3)

g + theme(panel.background = element_rect(fill = "grey60")) + 
    scale_color_gradient2(midpoint = mid, low = "blue4", mid = "white", high = "red4", "Ozone:")

Continuous Variables: Use the Beautiful Viridis Color Palette

The Viridis color palettes do not only make your plots look pretty and good to perceive but also easier to read by those with colorblindness and print well in grey scale:

Figure 1: Desaturated Color Palettes for Printing

Figure 2: Color Palettes Appearing under Green-Blindness (Deuteranopia)

(You can test how your plots might appear under various form of colorblindness using dichromate) package.)

The following multi-panel plot illustrates two out of the four viridis palettes:

library(viridis)
p1 <- g + scale_color_viridis("Ozone:") + ggtitle("Viridis 'default'")
p2 <- g + scale_color_viridis(option = "inferno", "Ozone:") + ggtitle("Viridis 'inferno'")
library(gridExtra)
grid.arrange(p1, p2, ncol = 2)

It is also possible to use the viridis color palettes for discrete variables:

ggplot(chic, aes(date, temp, color = factor(season))) +
   geom_point() + 
   labs(x = "Year", y = "Temperature") +
   theme(legend.title = element_blank(), 
         panel.background = element_rect(fill = "grey70"), 
         legend.key = element_rect(fill = "grey70")) +
   scale_color_viridis(discrete = T)

Working with Annotations

Add text annotation in the top-right, top-left etc.

With ggplot2 you can set annotation coordinates to Inf but this is only moderately useful. Here is an example (based on code from this Google group) using the library grid that allows you to specify the location based on scaled coordinates where 0 is low and 1 is high.

The grobTree function (from grid) creates a grid graphical object and textGrob creates the text graphical object. The annotation_custom() function comes from ggplot2 and is designed to use a grob as input.

library(grid)
my_grob = grobTree(textGrob("This text stays in place!", x = 0.1, y = 0.95, hjust = 0, gp = gpar(col = "blue", fontsize = 15, fontface = "italic")))

ggplot(chic, aes(temp, o3)) +
   geom_point(color = "firebrick") + 
   labs(x = "Temperature", y ="Ozone") +
   annotation_custom(my_grob)

The value of this is particularly evident when you have multiple plots with different scales. In the plot below you see that the axis scales vary yet the same code as above can be used to put the annotation is the same place on each facet.

ggplot(chic, aes(temp, o3)) +
   geom_point(color = "firebrick") + 
   labs(x = "Temperature", y ="Ozone") +
   facet_wrap(~season, scales = "free") +
   annotation_custom(my_grob)

Working with Coordinates

Flip a Plot

It is incredibly easy to flip your plot on its side. Here I have added the coord_flip() which is all you need to flip the plot (by the way, we are trying a new plot type by using geom_boxpot()).

ggplot(chic, aes(x = season, y = o3)) +
   geom_boxplot(fill = "indianred") + 
   labs(x = "Season", y = "Ozone") +
   coord_flip()

Working with Plot Types

Alternatives to The Box Plot

Box plots are great, but they can be so incredibly boring. There are alternatives, first â“– a common box plot:

g <- ggplot(chic, aes(x = season, y = o3)) + 
         labs(x = "Season", y = "Ozone")
g + geom_boxplot(fill = "indianred")

Effective? Yes.

Interesting? No.

1. Alternative: Plot of Points

g + geom_point(color = "firebrick")

Not only boring but uninformative. One could add transparency to deal with overplotting, but this is not good either.

2. Alternative: Jitter the Points

Try adding a little jitter to the data. I like this for in-house visualization but be careful using jittering because youâre purposely adding noise to your data and this can result in misinterpretation of your data.

g + geom_jitter(alpha = 0.5, aes(color = season), position = position_jitter(width = 0.6)) +
         theme(legend.title = element_blank())

3. Alternative: Violin Plots

Violin plots, similar to box plots except youâre using a kernel density to show where you have the most data, are a useful visualization.

g + geom_violin(color = "sienna", fill = "red", alpha = 0.4)

4. Alternative: Combining Violin Plots with Jitter

g + geom_violin(color = "gray", alpha = 0.5) +
    geom_jitter(aes(color = season), position = position_jitter(width = 0.3), alpha = 0.3) +
    theme(legend.title = element_blank()) +
    coord_flip()

Add a Ribbon to Your Plot (Addinga Given Range, AUC, CI, etc.)

This is not the perfect dataset for this, but using ribbon can be useful. In this example we will create a 30-day running average using the filter() function so that our ribbon is not too noisy.

chic$o3run <- as.numeric(filter(chic$o3, rep(1/30, 30), sides = 2))

ggplot(chic, aes(date, o3run)) +
   geom_line(color = "chocolate", lwd = 1) +
   labs(x = "Year", y = "Temperature")

How does it look if we fill in the area below the curve using the geom_ribbon() function?

ggplot(chic, aes(date, o3run)) +
   geom_ribbon(aes(ymin = 0, ymax = o3run), fill = "orange", color = "orange", alpha = 0.4) +
   geom_line(color = "chocolate", lwd = 1) +
   labs(x = "Year", y = "Temperature")

Nice to indicate the area under the curve (AUC) but this is not really the conventional way to use geom_ribbon(). Instead, we draw a ribbon that gives us one standard deviation above and below our data:

chic$mino3 <- chic$o3run - sd(chic$o3run, na.rm = T)
chic$maxo3 <- chic$o3run + sd(chic$o3run, na.rm = T)

ggplot(chic, aes(date, o3run)) +
   geom_ribbon(aes(ymin = mino3, ymax = maxo3), fill = "lightskyblue", color = "lightskyblue") +
   geom_line(color = "royalblue4", lwd = 0.7) +
   labs(x = "Year", y = "Temperature")

Create a Tiled Correlation Plot

First step is to create the correlation matrix. We are using Pearson because all the variables are fairly normally distributed â you may want to consider Spearman if your variables follow a different pattern. Note that since a correlation matrix has redundant information we are setting half of it to NA.

corm <- round(cor(chic[ ,sort(c("death", "temp", "dewpoint", "pm10", "o3"))], 
                  method = "pearson", use = "pairwise.complete.obs"), 2)
corm[lower.tri(corm)] <- NA
corm

##          death dewpoint    o3 pm10  temp
## death        1    -0.47 -0.24 0.00 -0.49
## dewpoint    NA     1.00  0.45 0.33  0.96
## o3          NA       NA  1.00 0.21  0.53
## pm10        NA       NA    NA 1.00  0.37
## temp        NA       NA    NA   NA  1.00

Now we put the resulting matrix in âlongâ format using the melt function from the reshape2 package and drop the records with NA values:

library(reshape2)
corm <- melt(corm)
corm$Var1 <- as.character(corm$Var1)
corm$Var2 <- as.character(corm$Var2)
corm <- na.omit(corm)
head(corm, 10)

##        Var1     Var2 value
## 1     death    death  1.00
## 6     death dewpoint -0.47
## 7  dewpoint dewpoint  1.00
## 11    death       o3 -0.24
## 12 dewpoint       o3  0.45
## 13       o3       o3  1.00
## 16    death     pm10  0.00
## 17 dewpoint     pm10  0.33
## 18       o3     pm10  0.21
## 19     pm10     pm10  1.00

For the plot we will use geom_tile but if you have a lot of data you might consider geom_raster which can be much faster.

ggplot(corm, aes(Var2, Var1)) +
   geom_tile(data = corm, aes(fill = value), color = "white") +
   labs(x = "Variable 2", y = "Variable 1") +
   scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
                        limit = c(-1, 1), name = "Correlation\n(Pearson)") +
   theme(axis.text.x = element_text(angle = 45, size = 11, vjust = 1, hjust = 1)) +
   coord_equal()

Working with Smoothings

It is amazingly easy to add a smoothing to your data using ggplot2. You can simply use stat_smooth() which will add a LOESS smooth if you have fewer than 1000 points or a GAM otherwise. Since we have more than 1000 points, the smoothing is a GAM.

Default: Adding a LOESS or GAM Smoothing

Here it is at its simplest – not even a formula required. For datasets with n < 1000 the default is set to loess, for datasets with 1000 or more observations to gam.

ggplot(chic, aes(date, temp)) + 
   geom_point(color="firebrick")+
   labs(x = "Year", y = "Temperature") +
   stat_smooth()

Specifying the Formula for Smoothing

But ggplot2 allows you to specify the model you want it to use. Letâs say you want to increase the GAM dimension (add some additional wiggles to the smooth):

ggplot(chic, aes(date, temp)) + 
   geom_point(color="grey60")+
   labs(x = "Year", y = "Temperature") +
   stat_smooth(method = "gam", formula = y~s(x, k = 1000), 
               se = F, size = 1.3, aes(col = "1000")) +
   stat_smooth(method = "gam", formula = y~s(x, k = 100), 
               se = F, size = 1, aes(col = "100")) +
   stat_smooth(method = "gam", formula = y~s(x, k = 10), 
               se = F, size = 0.8, aes(col = "10")) +
   scale_colour_manual(name = "k", values=c("darkorange1", "firebrick", "dodgerblue3"))

Adding a Linear Fit

Though the default is a smooth, it is also easy to add a standard linear fit:

ggplot(chic, aes(temp, death)) +
   geom_point(color = "firebrick") +
   labs(x = "Temperature", y = "Deaths") +
   stat_smooth(method = "lm", col = "darkorange1", se = F, size = 1.3)

Note that the same could be achieved using the more cumbersome:

lmTemp <- lm(death~temp, data = chic)

ggplot(chic, aes(temp, death)) + 
   geom_point(col = "firebrick") +
   labs(x = "Temperature", y = "Deaths") +
   geom_abline(intercept = lmTemp$coef[1], slope = lmTemp$coef[2], col = "darkorange1", size = 1.3)

Working with Interactive Graphs

Shiny

Shiny is a package from RStudio that makes it incredibly easy to build interactive web applications with R. For an introduction and live examples, visit the Shiny homepage.

To look at the potential use, you can check out the Hello Shiny examples. This is the first one:

library(shiny)
runExample("01_hello")

Plot.ly

Plot.ly is a great tool for easily creating online, interactive graphics directly from your ggplot2 plots. The process is surprisingly easy and can be done from within R.

ggplot2 Tutorial

Cedric Scherer (scherer@izw-berlin.de), updated 15. Januar 2017